"""simple docstring"""


import argparse

import requests
import torch
from PIL import Image
from torchvision.transforms import Compose, Normalize, Resize, ToTensor

from transformers import SwinaSRConfig, SwinaSRForImageSuperResolution, SwinaSRImageProcessor





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   str:




						'''simple docstring'''



						lowerCamelCase__   =SwinaSRConfig()

						if "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
												lowerCamelCase__   =4
						elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
												lowerCamelCase__   =4
												lowerCamelCase__   =48
												lowerCamelCase__   ="pixelshuffle_aux"
						elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
												lowerCamelCase__   =[6, 6, 6, 6]
												lowerCamelCase__   =60
												lowerCamelCase__   =[6, 6, 6, 6]
												lowerCamelCase__   ="pixelshuffledirect"
						elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
												lowerCamelCase__   =4
												lowerCamelCase__   ="nearest+conv"
						elif "Swin2SR_Jpeg_dynamic" in checkpoint_url:
												lowerCamelCase__   =1
												lowerCamelCase__   =1
												lowerCamelCase__   =126
												lowerCamelCase__   =7
												lowerCamelCase__   =255.0
												lowerCamelCase__   =""

						return config





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						if "patch_embed.proj" in name and "layers" not in name:
												lowerCamelCase__   =name.replace("patch_embed.proj"  ,       "embeddings.patch_embeddings.projection"							)
						if "patch_embed.norm" in name:
												lowerCamelCase__   =name.replace("patch_embed.norm"  ,       "embeddings.patch_embeddings.layernorm"							)
						if "layers" in name:
												lowerCamelCase__   =name.replace("layers"  ,       "encoder.stages"							)
						if "residual_group.blocks" in name:
												lowerCamelCase__   =name.replace("residual_group.blocks"  ,       "layers"							)
						if "attn.proj" in name:
												lowerCamelCase__   =name.replace("attn.proj"  ,       "attention.output.dense"							)
						if "attn" in name:
												lowerCamelCase__   =name.replace("attn"  ,       "attention.self"							)
						if "norm1" in name:
												lowerCamelCase__   =name.replace("norm1"  ,       "layernorm_before"							)
						if "norm2" in name:
												lowerCamelCase__   =name.replace("norm2"  ,       "layernorm_after"							)
						if "mlp.fc1" in name:
												lowerCamelCase__   =name.replace("mlp.fc1"  ,       "intermediate.dense"							)
						if "mlp.fc2" in name:
												lowerCamelCase__   =name.replace("mlp.fc2"  ,       "output.dense"							)
						if "q_bias" in name:
												lowerCamelCase__   =name.replace("q_bias"  ,       "query.bias"							)
						if "k_bias" in name:
												lowerCamelCase__   =name.replace("k_bias"  ,       "key.bias"							)
						if "v_bias" in name:
												lowerCamelCase__   =name.replace("v_bias"  ,       "value.bias"							)
						if "cpb_mlp" in name:
												lowerCamelCase__   =name.replace("cpb_mlp"  ,       "continuous_position_bias_mlp"							)
						if "patch_embed.proj" in name:
												lowerCamelCase__   =name.replace("patch_embed.proj"  ,       "patch_embed.projection"							)

						if name == "norm.weight":
												lowerCamelCase__   ="layernorm.weight"
						if name == "norm.bias":
												lowerCamelCase__   ="layernorm.bias"

						if "conv_first" in name:
												lowerCamelCase__   =name.replace("conv_first"  ,       "first_convolution"							)

						if (
						    "upsample" in name
						    or "conv_before_upsample" in name
						    or "conv_bicubic" in name
						    or "conv_up" in name
						    or "conv_hr" in name
						    or "conv_last" in name
						    or "aux" in name
						):
												# heads
												if "conv_last" in name:
																		lowerCamelCase__   =name.replace("conv_last"  ,       "final_convolution"							)
												if config.upsampler in ["pixelshuffle", "pixelshuffle_aux", "nearest+conv"]:
																		if "conv_before_upsample.0" in name:
																								lowerCamelCase__   =name.replace("conv_before_upsample.0"  ,       "conv_before_upsample"							)
																		if "upsample.0" in name:
																								lowerCamelCase__   =name.replace("upsample.0"  ,       "upsample.convolution_0"							)
																		if "upsample.2" in name:
																								lowerCamelCase__   =name.replace("upsample.2"  ,       "upsample.convolution_1"							)
																		lowerCamelCase__   ="upsample." + name
												elif config.upsampler == "pixelshuffledirect":
																		lowerCamelCase__   =name.replace("upsample.0.weight"  ,       "upsample.conv.weight"							)
																		lowerCamelCase__   =name.replace("upsample.0.bias"  ,       "upsample.conv.bias"							)
												else:
																		pass
						else:
												lowerCamelCase__   ="swin2sr." + name

						return name





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase							)				->   int:




						'''simple docstring'''



						for key in orig_state_dict.copy().keys():
												lowerCamelCase__   =orig_state_dict.pop(__lowerCAmelCase							)

												if "qkv" in key:
																		lowerCamelCase__   =key.split("."							)
																		lowerCamelCase__   =int(key_split[1]							)
																		lowerCamelCase__   =int(key_split[4]							)
																		lowerCamelCase__   =config.embed_dim

																		if "weight" in key:
																								lowerCamelCase__   =val[:dim, :]
																								lowerCamelCase__   =val[dim : dim * 2, :]
																								lowerCamelCase__   =val[-dim:, :]
																		else:
																								lowerCamelCase__   =val[:dim]
																								lowerCamelCase__   =val[dim : dim * 2]
																								lowerCamelCase__   =val[-dim:]
																		pass
												else:
																		lowerCamelCase__   =val

						return orig_state_dict





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase							)				->   str:




						'''simple docstring'''



						lowerCamelCase__   =get_config(__lowerCAmelCase							)
						lowerCamelCase__   =SwinaSRForImageSuperResolution(__lowerCAmelCase							)
						model.eval()

						lowerCamelCase__   =torch.hub.load_state_dict_from_url(__lowerCAmelCase  ,       map_location="cpu"							)
						lowerCamelCase__   =convert_state_dict(__lowerCAmelCase  ,       __lowerCAmelCase							)
						lowerCamelCase__   , lowerCamelCase__   =model.load_state_dict(__lowerCAmelCase  ,       strict=__lowerCAmelCase							)

						if len(__lowerCAmelCase							) > 0:
												raise ValueError("Missing keys when converting: {}".format(__lowerCAmelCase							)							)
						for key in unexpected_keys:
												if not ("relative_position_index" in key or "relative_coords_table" in key or "self_mask" in key):
																		raise ValueError(F'''Unexpected key {key} in state_dict'''							)

    # verify values
						lowerCamelCase__   ="https://github.com/mv-lab/swin2sr/blob/main/testsets/real-inputs/shanghai.jpg?raw=true"
						lowerCamelCase__   =Image.open(requests.get(__lowerCAmelCase  ,       stream=__lowerCAmelCase							).raw							).convert("RGB"							)
						lowerCamelCase__   =SwinaSRImageProcessor()
						# pixel_values = processor(image, return_tensors="pt").pixel_values

						lowerCamelCase__   =126 if "Jpeg" in checkpoint_url else 256
						lowerCamelCase__   =Compose(
						    [
						        Resize((image_size, image_size)							),
						        ToTensor(),
						        Normalize(mean=[0.485, 0.456, 0.406]  ,       std=[0.229, 0.224, 0.225]							),
						    ]							)
						lowerCamelCase__   =transforms(__lowerCAmelCase							).unsqueeze(0							)

						if config.num_channels == 1:
												lowerCamelCase__   =pixel_values[:, 0, :, :].unsqueeze(1							)

						lowerCamelCase__   =model(__lowerCAmelCase							)

						# assert values
						if "Swin2SR_ClassicalSR_X2_64" in checkpoint_url:
												lowerCamelCase__   =torch.Size([1, 3, 512, 512]							)
												lowerCamelCase__   =torch.tensor(
												    [[-0.7087, -0.7138, -0.6721], [-0.8340, -0.8095, -0.7298], [-0.9149, -0.8414, -0.7940]]							)
						elif "Swin2SR_ClassicalSR_X4_64" in checkpoint_url:
												lowerCamelCase__   =torch.Size([1, 3, 1024, 1024]							)
												lowerCamelCase__   =torch.tensor(
												    [[-0.7775, -0.8105, -0.8933], [-0.7764, -0.8356, -0.9225], [-0.7976, -0.8686, -0.9579]]							)
						elif "Swin2SR_CompressedSR_X4_48" in checkpoint_url:
												# TODO values didn't match exactly here
												lowerCamelCase__   =torch.Size([1, 3, 1024, 1024]							)
												lowerCamelCase__   =torch.tensor(
												    [[-0.8035, -0.7504, -0.7491], [-0.8538, -0.8124, -0.7782], [-0.8804, -0.8651, -0.8493]]							)
						elif "Swin2SR_Lightweight_X2_64" in checkpoint_url:
												lowerCamelCase__   =torch.Size([1, 3, 512, 512]							)
												lowerCamelCase__   =torch.tensor(
												    [[-0.7669, -0.8662, -0.8767], [-0.8810, -0.9962, -0.9820], [-0.9340, -1.0322, -1.1149]]							)
						elif "Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR" in checkpoint_url:
												lowerCamelCase__   =torch.Size([1, 3, 1024, 1024]							)
												lowerCamelCase__   =torch.tensor(
												    [[-0.5238, -0.5557, -0.6321], [-0.6016, -0.5903, -0.6391], [-0.6244, -0.6334, -0.6889]]							)

						assert (
						    outputs.reconstruction.shape == expected_shape
						), F'''Shape of reconstruction should be {expected_shape}, but is {outputs.reconstruction.shape}'''
						assert torch.allclose(outputs.reconstruction[0, 0, :3, :3]  ,       __lowerCAmelCase  ,       atol=1e-3							)
						print("Looks ok!"							)

						lowerCamelCase__   ={
						    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth": (
						        "swin2SR-classical-sr-x2-64"
						    ),
						    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X4_64.pth": (
						        "swin2SR-classical-sr-x4-64"
						    ),
						    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_CompressedSR_X4_48.pth": (
						        "swin2SR-compressed-sr-x4-48"
						    ),
						    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_Lightweight_X2_64.pth": (
						        "swin2SR-lightweight-x2-64"
						    ),
						    "https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_RealworldSR_X4_64_BSRGAN_PSNR.pth": (
						        "swin2SR-realworld-sr-x4-64-bsrgan-psnr"
						    ),
						}
						lowerCamelCase__   =url_to_name[checkpoint_url]

						if pytorch_dump_folder_path is not None:
												print(F'''Saving model {model_name} to {pytorch_dump_folder_path}'''							)
												model.save_pretrained(__lowerCAmelCase							)
												print(F'''Saving image processor to {pytorch_dump_folder_path}'''							)
												processor.save_pretrained(__lowerCAmelCase							)

						if push_to_hub:
												model.push_to_hub(F'''caidas/{model_name}'''							)
												processor.push_to_hub(F'''caidas/{model_name}'''							)


if __name__ == "__main__":
	a				=argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--checkpoint_url',
	    default='https://github.com/mv-lab/swin2sr/releases/download/v0.0.1/Swin2SR_ClassicalSR_X2_64.pth',
	    type=str,
	    help='URL of the original Swin2SR checkpoint you\'d like to convert.',
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
	)
	parser.add_argument('--push_to_hub', action='store_true', help='Whether to push the converted model to the hub.')

	a				=parser.parse_args()
	convert_swinasr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)



"""simple docstring"""


# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


a				={
    'configuration_mgp_str': ['MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MgpstrConfig'],
    'processing_mgp_str': ['MgpstrProcessor'],
    'tokenization_mgp_str': ['MgpstrTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a				=[
	    'MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'MgpstrModel',
	    'MgpstrPreTrainedModel',
	    'MgpstrForSceneTextRecognition',
	]

if TYPE_CHECKING:
	from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
	from .processing_mgp_str import MgpstrProcessor
	from .tokenization_mgp_str import MgpstrTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_mgp_str import (
		    MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
		    MgpstrForSceneTextRecognition,
		    MgpstrModel,
		    MgpstrPreTrainedModel,
		)
else:
	import sys

	a				=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)


